[论文解读] Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life

Marco De Nadai, Radu L. Vieriu+7 more|arXiv (Cornell University)|Aug 1, 2016

Urban Design and Spatial Analysis参考文献 41被引用 16

一句话总结

本研究通过结合移动电话数据（作为城市活力的代理指标）与基于谷歌街景图像训练的深度学习模型，探究了人们感知更安全的街区是否表现出更高的人员活动水平。结果显示，外观更安全的街区显著更活跃，尤其体现在女性和老年人群中，其中绿化和面向街道的窗户成为增强安全感感知的关键视觉线索。

ABSTRACT

Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of neighborhoods independent characteristics? Or are they just two sides of the same coin? In a world where people avoid unsafe looking places, neighborhoods that look unsafe will be less lively, and will fail to harness the natural surveillance of human activity. But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent. In this paper we explore the connection between the levels of activity and the perception of safety of neighborhoods in two major Italian cities by combining mobile phone data (as a proxy for activity or liveliness) with scores of perceived safety estimated using a Convolutional Neural Network trained on a dataset of Google Street View images scored using a crowdsourced visual perception survey. We find that: (i) safer looking neighborhoods are more active than what is expected from their population density, employee density, and distance to the city centre; and (ii) that the correlation between appearance of safety and activity is positive, strong, and significant, for females and people over 50, but negative for people under 30, suggesting that the behavioral impact of perception depends on the demographic of the population. Finally, we use occlusion techniques to identify the urban features that contribute to the appearance of safety, finding that greenery and street facing windows contribute to a positive appearance of safety (in agreement with Oscar Newman's defensible space theory). These results suggest that urban appearance modulates levels of human activity and, consequently, a neighborhood's rate of natural surveillance.

研究动机与目标

• 检验城市街区的感知安全性是否与实际的人类活动水平相关。
• 探究年龄和性别等人口统计因素如何调节感知安全性与街区活力之间的关系。
• 利用深度学习识别对安全感感知有贡献的具体视觉城市特征。
• 检验街区外观安全性是否在控制人口密度和市中心距离等标准城市指标后，仍能独立预测活动水平。

提出的方法

• 在众包评分的谷歌街景图像上训练卷积神经网络（CNN），以预测城市街区的感知安全性。
• 使用移动电话通话详单记录作为城市单元中人类活动（活力）的代理指标，覆盖罗马和米兰。
• 应用空间滤波的多元回归模型，隔离感知安全性对活动的影响，同时控制人口密度、就业密度和市中心距离等因素。
• 采用遮挡敏感性分析，识别影响CNN安全感感知预测结果最重要的图像区域。
• 针对不同人口群体（如30岁以下、50岁以上、女性）进行独立的回归分析，评估其对感知安全性的行为反应差异。
• 使用空间特征向量处理城市数据中的空间自相关性。

实验结果

研究问题

• RQ1 街区的感知安全性与其实际人类活动水平之间是否存在显著相关性？
• RQ2 安全感感知如何影响街区活动的人口构成，特别是按年龄和性别划分？
• RQ3 城市街景中的哪些具体视觉特征最强烈地促进安全感感知？
• RQ4 在控制关键城市密度和位置因素后，感知安全性对活力的影响是否依然显著？

主要发现

• 外观更安全的街区实际活动水平显著高于仅由人口密度、就业密度和市中心距离预测的水平。
• 感知安全性与活动之间的正相关关系在女性和50岁以上人群中最强烈，他们更倾向于前往外观更安全的区域。
• 对于30岁以下人群，感知安全性与活动水平呈负相关，表明年轻群体更可能在外观较不安全的街区活跃。
• 遮挡分析显示，面向街道的窗户和绿化是增强安全感感知的关键视觉特征，支持防卫空间理论。
• 在控制人口统计和地理变量后，街区外观安全性仍为活动水平的显著预测因子，表明其具有非平凡的行为影响。
• 活动的空间分布表现出显著的空间自相关性，该问题通过在回归模型中引入空间特征向量得到解决。